To resolve many business-related questions, a tool referred to as multidimensional analysis is used, which in SQL terms is a ‘group by’ operation. Generally for one query, a large amount of data is involved, whereby performance of the analysis is critical to obtain the results; for example, users cannot wait several hours to get analysis results.
Current OLAP (Online Analytical Processing) systems enhance the performance by pre-computing data cubes that correspond to the multidimensional arrangement of the data to be analyzed. More particularly, in OLAP, a dimension is a category of data represented in one column of a table, and a measure represents data in the table that can be accessed by specifying values for its dimensions. A set of measures having the same dimensions may be represented as an OLAP cube.
However, as the number of dimensions increases, the storage required for data cubes grows exponentially. As a result of this limitation, one cube can only support tens of dimensions. In certain types of analysis, for one query, the user can choose from thousands of dimensions, whereby OLAP is inadequate. The organization of such large amounts of data has a significant impact on the system performance.